Ajustes de inicio.
Notas del procesamiento de los datos
La comuna de Coihaique tiene dos estaciones: coyhaique y balmaceda. Por lo tanto, serían 26 comunas, no 27, ya que están asociadas solo a un código de comuna. Los datos descriptivos sugieren que la comuna de balmaceda es más fría. ¿Promediamos ambas estaciones?
Base de datos de análisis está filtrada por año de nacimiento 2011-2020 y código de 26 comunas. El número de observaciones es de: 394546.
Exploración de la tabla de datos
Exploración inicial
Tenemos 394546 observaciones y 16 variables. Todas estas variables son de tipo numérico que se categorizarán en función del análisis.
Click para ver el código
glimpse(births)Rows: 394.546
Columns: 16
$ sexo <dbl> 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1…
$ dia_nac <dbl> 14, 19, 8, 5, 12, 3, 6, 12, 14, 7, 23, 19, 28, 17, 9, 9, 2…
$ mes_nac <dbl> 7, 6, 12, 1, 3, 4, 11, 7, 9, 1, 2, 12, 2, 12, 9, 9, 6, 2, …
$ ano_nac <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018…
$ semanas <dbl> 40, 38, 40, 38, 40, 39, 33, 39, 39, 35, 38, 38, 39, 40, 38…
$ peso <dbl> 3290, 3660, 3390, 2740, 3080, 2885, 1210, 2890, 3330, 2730…
$ talla <dbl> 50, 49, 51, 48, 47, 47, 40, 48, 49, 47, 46, 51, 50, 48, 47…
$ comuna <dbl> 2201, 2101, 10101, 16101, 5101, 5101, 10101, 10101, 10101,…
$ region <dbl> 2, 2, 10, 16, 5, 5, 10, 10, 10, 16, 5, 5, 12, 10, 12, 12, …
$ edad_madre <dbl> 31, 35, 32, 27, 22, NA, 24, 20, 22, 28, 54, 39, 39, 39, 43…
$ nivel_madre <dbl> 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2…
$ edad_padre <dbl> 34, 42, 35, 29, 28, 32, 24, 19, 31, 30, 42, 44, 29, NA, 52…
$ nivel_padre <dbl> 2, 2, 1, 1, 1, 2, 2, 2, 4, 2, 1, 1, 2, NA, 2, 4, 1, 1, 2, …
$ activ_madre <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0…
$ tipo_parto <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ activ_padre <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, 1, 1, 1, NA, 1, 1, …
Se recodifican valores que son considerados como missing para variables de interés:
births <- births %>%
mutate(semanas=if_else(semanas==99, NA_real_, semanas),
peso=if_else(peso==9999, NA_real_, peso),
sexo=if_else(sexo==9, NA_real_, sexo),
talla=if_else(talla==99, NA_real_, talla),
edad_madre=if_else(edad_madre==99, NA_real_, edad_madre),
nivel_madre=if_else(nivel_madre==9, NA_real_, nivel_madre),
activ_madre=if_else(activ_madre %in% c(3,9), NA_real_, activ_madre),
edad_padre=if_else(edad_padre==99, NA_real_, edad_padre),
nivel_padre=if_else(nivel_padre==9, NA_real_, nivel_padre),
activ_padre=if_else(activ_padre %in% c(3,9), NA_real_, activ_padre),
tipo_parto=if_else(tipo_parto==9, NA_real_, tipo_parto)
)
births <- births[,c(1:9, 15, 10, 11, 14, 12, 13, 16)]Valores extremos
Se evaluarán los casos extremos para dos variables de interés: semanas de gestación y peso al nacer.
Click para ver el código
g1 <- ggplot(births, aes(x=semanas)) +
geom_histogram(alpha=0.5, bins=50, fill = "deepskyblue3", color="deepskyblue3") +
scale_x_continuous(breaks=seq(0, 50, by=10)) +
labs(x="Semanas de gestación", y="Frecuencia", tag="A)") +
theme_light() +
theme(panel.background = element_rect(fill = "white"),
panel.grid = element_blank(),
legend.position = "top",
legend.background = element_rect(fill = alpha("white", 0.0), color = alpha("white", 0.5)),
strip.background = element_rect(fill="white", color="white"),
strip.text.x = element_text(size=10, hjust = 0, color="black"),
legend.key.width = unit(1.5,"cm"),
plot.tag=element_text(size=16, face="bold"),
axis.line.x = element_blank())
g2 <- ggplot(births, aes(y=semanas)) +
stat_boxplot(geom = "errorbar", width = 0.15, color = 1) +
geom_boxplot(fill = "gray", alpha = 0.5, color = "black", width=0.7,
outlier.colour = 2, outlier.fill = "white", outlier.size = .75) +
scale_y_continuous(breaks=seq(0, 100, by=5)) +
labs(x="Semanas de gestación", y=NULL, tag="B)", x="") +
theme_light() +
theme(panel.background = element_rect(fill = "white"),
panel.grid = element_blank(),
legend.position = "top",
legend.background = element_rect(fill = alpha("white", 0.0), color = alpha("white", 0.5)),
strip.background = element_rect(fill="white", color="white"),
strip.text.x = element_text(size=10, hjust = 0, color="black"),
legend.key.width = unit(1.5,"cm"),
plot.tag=element_text(size=16, face="bold"),
axis.line.x = element_blank())
ggpubr::ggarrange(g1, g2, ncol = 2, widths = c(2, 1))Click para ver el código
g3 <- ggplot(births, aes(x=peso)) +
geom_histogram(alpha=0.5, bins=150, fill = "deepskyblue3", color="deepskyblue3") +
scale_x_continuous(breaks=seq(0, 10000, by=1000)) +
labs(x="Peso (gr.)", y="Frecuencia", tag="A)") +
theme_light() +
theme(panel.background = element_rect(fill = "white"),
panel.grid = element_blank(),
legend.position = "top",
legend.background = element_rect(fill = alpha("white", 0.0), color = alpha("white", 0.5)),
strip.background = element_rect(fill="white", color="white"),
strip.text.x = element_text(size=10, hjust = 0, color="black"),
legend.key.width = unit(1.5,"cm"),
plot.tag=element_text(size=16, face="bold"),
axis.line.x = element_blank())
g4 <- ggplot(births, aes(y=peso)) +
stat_boxplot(geom = "errorbar", width = 0.15, color = 1) +
geom_boxplot(fill = "gray", alpha = 0.5, color = "black", width=0.7,
outlier.colour = 2, outlier.fill = "white", outlier.size = .75) +
scale_y_continuous(breaks=seq(0, 10000, by=1000)) +
labs(y="Peso (gr.)", y=NULL, tag="B)", x="") +
theme_light() +
theme(panel.background = element_rect(fill = "white"),
panel.grid = element_blank(),
legend.position = "top",
legend.background = element_rect(fill = alpha("white", 0.0), color = alpha("white", 0.5)),
strip.background = element_rect(fill="white", color="white"),
strip.text.x = element_text(size=10, hjust = 0, color="black"),
legend.key.width = unit(1.5,"cm"),
plot.tag=element_text(size=16, face="bold"),
axis.line.x = element_blank())
ggpubr::ggarrange(g3, g4, ncol = 2, widths = c(2, 1))Click para ver el código
vars <- c("semanas", "peso")
table <- tibble()
for (i in vars){
# Asegúrate de que i es convertido a simbolo con as.symbol o rlang::sym
des_res <- descriptives(x = !!rlang::sym(i), data = births)
table <- dplyr::bind_rows(table, des_res)
}
table$Variable <- c("Gestación (semanas)", "Peso (gr.)")
flextable::flextable(table)Variable |
Media_Prop |
SD |
Min |
P5 |
P10 |
P25 |
P50 |
P75 |
P90 |
P95 |
Max |
N |
Missing |
Pct_miss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Gestación (semanas) |
38.357 |
1.868 |
16 |
36 |
37 |
38 |
39 |
39 |
40 |
40 |
44 |
394,546 |
648 |
0.16 |
Peso (gr.) |
3,294.457 |
549.190 |
151 |
2,380 |
2,675 |
3,020 |
3,330 |
3,635 |
3,925 |
4,100 |
5,740 |
394,546 |
658 |
0.17 |
Datos faltantes
Exploración de datos faltantes para la información del padre y madre:
Click para ver el código
covariables <- births[, c(11:16)]
g5 <- vis_miss(births, warn_large_data = FALSE)
g6 <- gg_miss_upset(births, order.by = "freq", nintersects = NA, empty.intersections=TRUE)
g5Click para ver el código
g6Click para ver el código
missing_data <- births %>%
select(4:7, 10:16) %>%
pivot_longer(cols = -ano_nac, names_to = "variable", values_to = "valor") %>%
group_by(ano_nac, variable) %>%
summarise(porcentaje_missing = mean(is.na(valor)) * 100) %>%
ungroup()
ggplot(missing_data, aes(x = factor(ano_nac), y = porcentaje_missing, group = variable)) +
geom_line(aes(color = variable)) +
geom_point(aes(color = variable)) +
facet_wrap(~ variable, scales = "free_y", ncol=5) +
theme_light() +
labs(x = "Año", y = "% de Datos Faltantes") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_blank(),
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(color="black")) Click para ver el código
comunas <- chilemapas::codigos_territoriales %>%
mutate(codigo_comuna=as.numeric(codigo_comuna),
codigo_provincia=as.numeric(codigo_provincia),
codigo_region=as.numeric(codigo_region)
)
births <- births %>%
left_join(comunas, by=c("comuna"="codigo_comuna"))
missing_data <- births %>%
select(5:7, 10:16, 17) %>%
pivot_longer(cols = -nombre_comuna, names_to = "variable", values_to = "valor") %>%
group_by(nombre_comuna, variable) %>%
summarise(porcentaje_missing = mean(is.na(valor)) * 100) %>%
ungroup()
ggplot(missing_data, aes(x = factor(nombre_comuna), y = porcentaje_missing, fill = variable)) +
geom_col() +
coord_flip() +
facet_wrap(~ variable, scales = "free_y", ncol=5) +
theme_light() +
labs(x = "Comuna", y = "% de Datos Faltantes") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_blank(),
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(color="black")) Estadísticos Descriptivos
Para estos análisis construimos una variable nueva para identificar los nacimientos con bajo peso (<2500 gr.). Además, construimos una variable que identifican las macrozonas.
Click para ver el código
births <- births %>%
mutate(
low_weight=case_when(
peso<2500 ~ 1,
peso>=2500 ~ 0,
TRUE ~ NA_real_
),
zona = case_when(
nombre_region %in% c("Arica y Parinacota", "Tarapacá", "Antofagasta", "Atacama", "Coquimbo") ~ "Norte",
nombre_region %in% c("Valparaiso", "Metropolitana de Santiago", "Libertador General Bernardo O'Higgins", "Maule", "Nuble", "Biobio") ~ "Centro",
nombre_region %in% c("La Araucanía", "Los Ríos", "Los Lagos", "Aysen del General Carlos Ibanez del Campo", "Magallanes y de la Antartica Chilena") ~ "Sur",
TRUE ~ NA_character_
)
#zona=factor(zona, levels=c("Norte", "Centro", "Sur", "Austral"))
) Descripción de variables
Click para ver el código
# Generamos dummies
births <- cbind(births, make_dummies(births$sexo, prefix = ""))
births <- cbind(births, make_dummies(births$tipo_parto, prefix = ""))
births <- cbind(births, make_dummies(births$zona, prefix = ""))
births <- cbind(births, make_dummies(births$nombre_region, prefix = ""))
births <- cbind(births, make_dummies(births$nivel_madre, prefix = ""))
births <- cbind(births, make_dummies(births$activ_madre, prefix = ""))
births <- cbind(births, make_dummies(births$nivel_padre, prefix = ""))
births <- cbind(births, make_dummies(births$activ_padre, prefix = ""))
# Data
births_full <- births %>%
select("semanas", "peso", "low_weight", "talla",
"sexo1", "sexo2", "tipo_parto1", "tipo_parto2", "tipo_parto3", "tipo_parto4",
"edad_madre",
"nivel_madre1", "nivel_madre2", "nivel_madre3", "nivel_madre4", "nivel_madre5",
"activ_madre0", "activ_madre1", "activ_madre2", # "activ_madre3",
"edad_padre",
"nivel_padre1", "nivel_padre2", "nivel_padre3", "nivel_padre4", "nivel_padre5",
"activ_padre0", "activ_padre1", "activ_padre2",
"nombre_regionArica y Parinacota", "nombre_regionAntofagasta",
"nombre_regionValparaiso", "nombre_regionMetropolitana de Santiago", "nombre_regionNuble",
"nombre_regionBiobio", "nombre_regionMaule", "nombre_regionLos Lagos",
"nombre_regionAysen del General Carlos Ibanez del Campo", "nombre_regionMagallanes y de la Antartica Chilena",
"zonaNorte", "zonaCentro", "zonaSur"
)
vars_des <- colnames(births_full)
table <- tibble()
for (i in vars_des){
# Asegúrate de que i es convertido a simbolo con as.symbol o rlang::sym
des_res <- descriptives(x = !!rlang::sym(i), data = births_full)
table <- dplyr::bind_rows(table, des_res)
}
table$Variable <- c("Gestación (semanas)", "Peso (gr.)", "Peso <2500 gr.", "Talla (cm.)",
"Hombre", "Mujer",
"Parto Simple", "Parto Doble", "Parto Triple", "Otro",
"Edad de la madre",
"Educ. Madre Superior", "Medio", "Secundaria", "Básico o Primario", "Sin educación",
"Act. Madre: Inactivo", "Activo", "Cesante o desocupado",
"Edad del padre",
"Educ. Padre Superior", "Medio", "Secundaria", "Básico o Primario", "Sin educación",
"Act. Padre: Inactivo", "Activo", "Cesante o desocupado",
"Arica y Parinacota", "Antofagasta",
"Valparaiso", "Metropolitana de Santiago", "Nuble",
"Biobio", "Maule", "Los Lagos",
"Aysen del General Carlos Ibanez del Campo", "Magallanes y de la Antartica Chilena",
"Norte", "Centro", "Sur"
)
table <- table %>%
mutate(across(c(4:12), ~na_if(.x, 0))) %>%
mutate(across(c(4:12), ~na_if(.x, 1))) %>%
select(-"N")
flextable::flextable(table)Variable |
Media_Prop |
SD |
Min |
P5 |
P10 |
P25 |
P50 |
P75 |
P90 |
P95 |
Max |
Missing |
Pct_miss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Gestación (semanas) |
38.357 |
1.868 |
16 |
36 |
37 |
38 |
39 |
39 |
40 |
40 |
44 |
648 |
0.16 |
Peso (gr.) |
3,294.457 |
549.190 |
151 |
2,380 |
2,675 |
3,020 |
3,330 |
3,635 |
3,925 |
4,100 |
5,740 |
658 |
0.17 |
Peso <2500 gr. |
0.065 |
0.246 |
658 |
0.17 |
|||||||||
Talla (cm.) |
49.011 |
2.551 |
17 |
45 |
47 |
48 |
49 |
50 |
52 |
52 |
59 |
653 |
0.17 |
Hombre |
0.510 |
0.500 |
32 |
0.01 |
|||||||||
Mujer |
0.490 |
0.500 |
32 |
0.01 |
|||||||||
Parto Simple |
0.980 |
0.142 |
68,550 |
17.37 |
|||||||||
Parto Doble |
0.020 |
0.140 |
68,550 |
17.37 |
|||||||||
Parto Triple |
0.000 |
0.019 |
68,550 |
17.37 |
|||||||||
Otro |
0.000 |
0.006 |
68,550 |
17.37 |
|||||||||
Edad de la madre |
28.004 |
6.495 |
11 |
18 |
19 |
23 |
28 |
33 |
37 |
39 |
59 |
191 |
0.05 |
Educ. Madre Superior |
0.395 |
0.489 |
830 |
0.21 |
|||||||||
Medio |
0.521 |
0.500 |
830 |
0.21 |
|||||||||
Secundaria |
0.000 |
0.017 |
830 |
0.21 |
|||||||||
Básico o Primario |
0.083 |
0.275 |
830 |
0.21 |
|||||||||
Sin educación |
0.001 |
0.026 |
830 |
0.21 |
|||||||||
Act. Madre: Inactivo |
0.499 |
0.500 |
7,723 |
1.96 |
|||||||||
Activo |
0.500 |
0.500 |
7,723 |
1.96 |
|||||||||
Cesante o desocupado |
0.001 |
0.038 |
7,723 |
1.96 |
|||||||||
Edad del padre |
30.885 |
7.569 |
14 |
20 |
21 |
25 |
30 |
36 |
41 |
44 |
84 |
37,575 |
9.52 |
Educ. Padre Superior |
0.399 |
0.490 |
38,048 |
9.64 |
|||||||||
Medio |
0.518 |
0.500 |
38,048 |
9.64 |
|||||||||
Secundaria |
0.001 |
0.028 |
38,048 |
9.64 |
|||||||||
Básico o Primario |
0.082 |
0.274 |
38,048 |
9.64 |
|||||||||
Sin educación |
0.001 |
0.023 |
38,048 |
9.64 |
|||||||||
Act. Padre: Inactivo |
0.151 |
0.358 |
40,850 |
10.35 |
|||||||||
Activo |
0.838 |
0.368 |
40,850 |
10.35 |
|||||||||
Cesante o desocupado |
0.011 |
0.105 |
40,850 |
10.35 |
|||||||||
Arica y Parinacota |
0.082 |
0.275 |
0 |
0.00 |
|||||||||
Antofagasta |
0.207 |
0.405 |
0 |
0.00 |
|||||||||
Valparaiso |
0.096 |
0.295 |
0 |
0.00 |
|||||||||
Metropolitana de Santiago |
0.144 |
0.351 |
0 |
0.00 |
|||||||||
Nuble |
0.059 |
0.236 |
0 |
0.00 |
|||||||||
Biobio |
0.134 |
0.341 |
0 |
0.00 |
|||||||||
Maule |
0.052 |
0.222 |
0 |
0.00 |
|||||||||
Los Lagos |
0.144 |
0.351 |
0 |
0.00 |
|||||||||
Aysen del General Carlos Ibanez del Campo |
0.032 |
0.175 |
0 |
0.00 |
|||||||||
Magallanes y de la Antartica Chilena |
0.048 |
0.215 |
0 |
0.00 |
|||||||||
Norte |
0.290 |
0.454 |
0 |
0.00 |
|||||||||
Centro |
0.486 |
0.500 |
0 |
0.00 |
|||||||||
Sur |
0.224 |
0.417 |
0 |
0.00 |
Tendencia espacio-temporales
Click para ver el código
births <- births %>%
mutate(date_nac = make_date(year = ano_nac, month = mes_nac, day = dia_nac))
nac <- births %>%
group_by(ano_nac, mes_nac, dia_nac, date_nac) %>%
summarise(n=n(),
peso=mean(peso, na.rm = TRUE),
low_weight=round(mean(low_weight , na.rm = TRUE),3)
)
ggplot(nac, aes(x = date_nac, y = n)) +
geom_line() +
scale_x_date(date_breaks = "1 month", date_labels = "%b") +
facet_wrap(~ ano_nac, scales = "free_x", ncol=2) +
labs(x = "Fecha", y = "Cantidad de nacimientos") +
theme_light() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_blank(),
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(color="black")) Click para ver el código
ggplot(nac, aes(x = date_nac, y = peso)) +
geom_line() +
scale_x_date(date_breaks = "1 month", date_labels = "%b") +
facet_wrap(~ ano_nac, scales = "free_x", ncol=2) +
labs(x = "Fecha", y = "Peso (gramos)") +
theme_light() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_blank(),
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(color="black")) Click para ver el código
ggplot(nac, aes(x = date_nac, y = low_weight, color=factor(ano_nac))) +
geom_col() +
scale_y_continuous(labels = scales::percent_format()) +
scale_x_date(date_breaks = "1 month", date_labels = "%b") +
#coord_flip() +
facet_wrap(~ ano_nac, scales = "free_x", ncol=2) +
labs(x = "Fecha", y = "% de nacimientos con bajo peso") +
theme_light() +
theme(axis.text.x = element_text(angle = 0, hjust = 1),
legend.title = element_blank(),
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(color="black")) Click para ver el código
Muestra |
Nacimientos |
Peso |
Bajo_peso |
|---|---|---|---|
Full Sample |
394,546 |
3,294.457 |
0.065 |
Click para ver el código
nac_zona <- births %>%
group_by(zona) %>%
summarise(Nacimientos=n(),
Peso=mean(peso, na.rm = TRUE),
Bajo_peso=round(mean(low_weight , na.rm = TRUE),3)
)%>%
mutate(Muestra="Macrozona") %>%
relocate(Muestra) %>%
rename(Zona=zona)
nac_reg <- births %>%
group_by(nombre_region) %>%
summarise(Nacimientos=n(),
Peso=mean(peso, na.rm = TRUE),
Bajo_peso=round(mean(low_weight , na.rm = TRUE),3)
) %>%
mutate(Muestra="Región") %>%
relocate(Muestra) %>%
rename(Zona=nombre_region)
nac_zona_reg <- nac_zona %>% bind_rows(nac_reg)
flextable::flextable(nac_zona_reg)Muestra |
Zona |
Nacimientos |
Peso |
Bajo_peso |
|---|---|---|---|---|
Macrozona |
Centro |
191,782 |
3,294.815 |
0.066 |
Macrozona |
Norte |
114,281 |
3,268.147 |
0.065 |
Macrozona |
Sur |
88,483 |
3,327.654 |
0.062 |
Región |
Antofagasta |
81,828 |
3,226.634 |
0.071 |
Región |
Arica y Parinacota |
32,453 |
3,372.957 |
0.050 |
Región |
Aysen del General Carlos Ibanez del Campo |
12,433 |
3,345.040 |
0.060 |
Región |
Biobio |
53,031 |
3,290.294 |
0.063 |
Región |
Los Lagos |
56,922 |
3,328.514 |
0.062 |
Región |
Magallanes y de la Antartica Chilena |
19,128 |
3,313.798 |
0.063 |
Región |
Maule |
20,498 |
3,258.138 |
0.068 |
Región |
Metropolitana de Santiago |
56,893 |
3,301.640 |
0.066 |
Región |
Nuble |
23,380 |
3,309.228 |
0.063 |
Región |
Valparaiso |
37,980 |
3,301.863 |
0.071 |
Click para ver el código
Agno |
Mes |
Nacimientos |
Peso |
Bajo_peso |
|---|---|---|---|---|
2011 |
1 |
3,878 |
3,335.667 |
0.050 |
2011 |
2 |
3,436 |
3,317.982 |
0.065 |
2011 |
3 |
4,014 |
3,326.390 |
0.057 |
2011 |
4 |
3,538 |
3,310.935 |
0.058 |
2011 |
5 |
3,656 |
3,304.859 |
0.061 |
2011 |
6 |
3,446 |
3,311.191 |
0.067 |
2011 |
7 |
3,468 |
3,314.592 |
0.055 |
2011 |
8 |
3,570 |
3,304.252 |
0.073 |
2011 |
9 |
3,736 |
3,325.636 |
0.053 |
2011 |
10 |
3,561 |
3,311.560 |
0.062 |
2011 |
11 |
3,402 |
3,316.736 |
0.060 |
2011 |
12 |
3,511 |
3,300.352 |
0.063 |
2012 |
1 |
3,681 |
3,307.591 |
0.057 |
2012 |
2 |
3,396 |
3,304.378 |
0.065 |
2012 |
3 |
3,696 |
3,302.532 |
0.062 |
2012 |
4 |
3,607 |
3,297.513 |
0.063 |
2012 |
5 |
3,637 |
3,307.468 |
0.055 |
2012 |
6 |
3,509 |
3,296.902 |
0.063 |
2012 |
7 |
3,595 |
3,303.769 |
0.061 |
2012 |
8 |
3,657 |
3,312.963 |
0.066 |
2012 |
9 |
3,507 |
3,303.137 |
0.068 |
2012 |
10 |
3,631 |
3,318.197 |
0.058 |
2012 |
11 |
3,442 |
3,298.229 |
0.069 |
2012 |
12 |
3,344 |
3,307.643 |
0.065 |
2013 |
1 |
3,691 |
3,303.710 |
0.063 |
2013 |
2 |
3,252 |
3,313.535 |
0.062 |
2013 |
3 |
3,549 |
3,293.758 |
0.068 |
2013 |
4 |
3,509 |
3,308.330 |
0.053 |
2013 |
5 |
3,560 |
3,299.586 |
0.061 |
2013 |
6 |
3,461 |
3,310.532 |
0.066 |
2013 |
7 |
3,525 |
3,296.095 |
0.061 |
2013 |
8 |
3,471 |
3,290.056 |
0.066 |
2013 |
9 |
3,580 |
3,301.039 |
0.061 |
2013 |
10 |
3,547 |
3,294.147 |
0.063 |
2013 |
11 |
3,377 |
3,299.130 |
0.063 |
2013 |
12 |
3,484 |
3,303.892 |
0.061 |
2014 |
1 |
3,826 |
3,301.906 |
0.064 |
2014 |
2 |
3,210 |
3,340.594 |
0.052 |
2014 |
3 |
3,697 |
3,300.302 |
0.064 |
2014 |
4 |
3,611 |
3,304.498 |
0.062 |
2014 |
5 |
3,586 |
3,305.751 |
0.065 |
2014 |
6 |
3,646 |
3,291.106 |
0.061 |
2014 |
7 |
3,684 |
3,293.995 |
0.066 |
2014 |
8 |
3,496 |
3,314.393 |
0.063 |
2014 |
9 |
3,666 |
3,314.448 |
0.057 |
2014 |
10 |
3,734 |
3,309.616 |
0.064 |
2014 |
11 |
3,426 |
3,292.717 |
0.068 |
2014 |
12 |
3,627 |
3,295.435 |
0.059 |
2015 |
1 |
3,851 |
3,305.704 |
0.066 |
2015 |
2 |
3,403 |
3,301.912 |
0.059 |
2015 |
3 |
3,729 |
3,290.289 |
0.060 |
2015 |
4 |
3,563 |
3,297.320 |
0.059 |
2015 |
5 |
3,443 |
3,266.271 |
0.064 |
2015 |
6 |
3,490 |
3,281.349 |
0.064 |
2015 |
7 |
3,485 |
3,293.414 |
0.066 |
2015 |
8 |
3,435 |
3,285.833 |
0.074 |
2015 |
9 |
3,650 |
3,288.337 |
0.066 |
2015 |
10 |
3,350 |
3,282.810 |
0.075 |
2015 |
11 |
3,156 |
3,297.625 |
0.064 |
2015 |
12 |
3,425 |
3,264.310 |
0.073 |
2016 |
1 |
3,516 |
3,272.472 |
0.077 |
2016 |
2 |
3,279 |
3,281.303 |
0.063 |
2016 |
3 |
3,643 |
3,291.265 |
0.064 |
2016 |
4 |
3,295 |
3,284.641 |
0.063 |
2016 |
5 |
3,318 |
3,293.017 |
0.067 |
2016 |
6 |
3,282 |
3,270.509 |
0.071 |
2016 |
7 |
3,206 |
3,277.219 |
0.067 |
2016 |
8 |
3,243 |
3,287.046 |
0.071 |
2016 |
9 |
3,410 |
3,297.618 |
0.060 |
2016 |
10 |
3,087 |
3,295.789 |
0.057 |
2016 |
11 |
3,062 |
3,302.436 |
0.059 |
2016 |
12 |
3,188 |
3,286.123 |
0.059 |
2017 |
1 |
3,183 |
3,277.633 |
0.071 |
2017 |
2 |
2,903 |
3,267.309 |
0.073 |
2017 |
3 |
3,316 |
3,273.892 |
0.067 |
2017 |
4 |
3,022 |
3,294.342 |
0.066 |
2017 |
5 |
3,099 |
3,294.879 |
0.065 |
2017 |
6 |
3,152 |
3,301.257 |
0.063 |
2017 |
7 |
3,105 |
3,275.950 |
0.070 |
2017 |
8 |
3,138 |
3,278.222 |
0.068 |
2017 |
9 |
3,078 |
3,290.095 |
0.069 |
2017 |
10 |
3,004 |
3,294.465 |
0.067 |
2017 |
11 |
2,943 |
3,288.965 |
0.064 |
2017 |
12 |
3,037 |
3,278.918 |
0.068 |
2018 |
1 |
3,141 |
3,279.956 |
0.070 |
2018 |
2 |
2,896 |
3,285.765 |
0.069 |
2018 |
3 |
3,163 |
3,279.670 |
0.068 |
2018 |
4 |
2,927 |
3,292.971 |
0.057 |
2018 |
5 |
3,204 |
3,293.212 |
0.067 |
2018 |
6 |
3,048 |
3,281.207 |
0.074 |
2018 |
7 |
3,046 |
3,278.960 |
0.072 |
2018 |
8 |
3,128 |
3,264.423 |
0.075 |
2018 |
9 |
3,165 |
3,278.846 |
0.066 |
2018 |
10 |
3,153 |
3,288.751 |
0.063 |
2018 |
11 |
2,986 |
3,273.588 |
0.079 |
2018 |
12 |
3,093 |
3,259.123 |
0.080 |
2019 |
1 |
3,223 |
3,286.213 |
0.069 |
2019 |
2 |
2,855 |
3,287.216 |
0.065 |
2019 |
3 |
3,065 |
3,293.382 |
0.067 |
2019 |
4 |
2,923 |
3,285.807 |
0.071 |
2019 |
5 |
3,019 |
3,294.099 |
0.064 |
2019 |
6 |
2,913 |
3,288.920 |
0.065 |
2019 |
7 |
2,964 |
3,262.312 |
0.074 |
2019 |
8 |
2,863 |
3,285.945 |
0.066 |
2019 |
9 |
2,944 |
3,279.752 |
0.068 |
2019 |
10 |
2,920 |
3,302.006 |
0.066 |
2019 |
11 |
2,714 |
3,272.672 |
0.068 |
2019 |
12 |
2,855 |
3,273.638 |
0.069 |
2020 |
1 |
3,011 |
3,286.824 |
0.064 |
2020 |
2 |
2,617 |
3,289.305 |
0.062 |
2020 |
3 |
2,884 |
3,277.622 |
0.069 |
2020 |
4 |
2,711 |
3,287.094 |
0.067 |
2020 |
5 |
2,724 |
3,290.064 |
0.066 |
2020 |
6 |
2,775 |
3,283.157 |
0.063 |
2020 |
7 |
2,699 |
3,278.303 |
0.073 |
2020 |
8 |
2,654 |
3,277.549 |
0.071 |
2020 |
9 |
2,750 |
3,295.568 |
0.070 |
2020 |
10 |
2,717 |
3,291.117 |
0.061 |
2020 |
11 |
2,606 |
3,298.952 |
0.064 |
2020 |
12 |
2,568 |
3,288.356 |
0.071 |